Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed
intelligence of industrial automation thanks to its capability of distributed machine learning …

In this paper, we aim to make the best joint decision of device selection and computing and
spectrum resource allocation for optimizing federated learning (FL) performance in …

In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …

Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0
and produced massive amounts of data for industrial intelligence. These data locate on …

Federated learning (FL) is a promising distributed learning approach which enables multiple
devices to collaboratively train deep neural networks in a privacy-preserving fashion …

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-
device learning paradigm. However, multiple challenges remain to be addressed for …

Federated learning (FL) is a promising paradigm to empower on-device intelligence in
Industrial Internet of Things (IIoT) due to its capability of training machine learning models …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Federated learning (FL) enables wireless terminals to collaboratively learn a shared
parameter model while keeping all the training data on devices per se. Parameter sharing …